Embodiments of the invention relate to simplified graphical analysis of multiple data series.
Extract, Transformation and Load (ETL) processes are often executed on a repeating basis, where a particular ETL process is executed in a runtime environment, which is shared with many other system resource-intensive activities. Given varying system load, the ETL user may want to monitor variances across a range of ETL process runtime metrics (i.e., “data series” in graphing terms) to identify performance problems or bottlenecks caused by overloaded system resources and/or by other causes.
Herein, the terms “metric/data series” may be used. Both “metric” and “data series” describe a measurement that may be used to gauge some quantifiable component of performance of an ETL process, but “data series” may be described as a set of data for this measurement that may be used to create a graph.
A typical ETL process may utilize a range of system resources, including varying aspects of: Central Processing Unit (CPU), memory, disk Input/Output (I/O), and disk space. In addition, various metrics/data series related to the performance of the ETL process itself are of interest, including, but not limited to: total data rows processed (Total Rows); total number of rows processed on the input (Rows In); total number of rows processed on the output (Rows Out) and an indication of current performance (Rows per second). The measurements of systems resources used by the ETL process and related to the performance of the ETL process may be described as current metrics/data series for a particular measurement of an Extract, Transformation, Load (ETL) process.
In order to monitor variances in these repeating process executions, the ETL user may want to compare and analyze the ETL process execution related metrics/data series across two or more executions of a given ETL process. Moreover, the ETL user may want to carry out this comparison and analysis via a Web 2.0 based User Interface (UI) hosted on a remote computer via a client session. Analysis may be done by creating graphs of each specific metric/data series for each of the ETL process executions, so that the ETL user can see the variance in the metric/data series over time.
With traditional graphing tools, graphs can be displayed for different metrics/data series in an overlay. Graphical User Interface (GUI) buttons may be used to select/deselect metrics/data series for display in a composite (overlay) graph. The user activates a metric/data series for display in order to get an indication of the metric/data series performance.
Thus, there can be numerous metrics/data series involved when analyzing a typical ETL process execution. When large numbers of metrics/data series are presented using traditional graphing tools, the graphical view presented to the user may be congested. Similarly, adding increasing numbers of metrics/data series to a graph can result in a congested graphical view. In such congested graphical views, it may be difficult to differentiate one metrics/data series from another.
Traditional graphing tools enable simplifying the graphing view by removing metrics/data series from being included in the graph. However, once a metric/data series is removed from the graph, the user then loses a graphical view on that removed metric/data series.